How Do Depression Medications Taken by Pilots Affect Passengers’ Willingness to Fly—A Mediation Analysis
Bibliographic record
Abstract
<p>The mental health of airline pilots has been a concern for decades. In 2010, the United States Federal Aviation Administration began allowing four types of selective serotonin reuptake inhibitors (SSRIs) to be used by pilots suffering from depression. After a procedural wait period, pilots may be awarded a special issuance of their medical certificates to maintain flight currency. Missing from the literature was any research on consumer’s perceptions of pilots taking antidepressants, along with some other approved medications. Therefore, the purpose of the current study was to examine consumer’s willingness to fly once told that the pilot of their hypothetical flight was taking medication compared to a control group in which the pilot was not on any prescribed and approved medications. The current study also manipulated dosage levels and gathered affect data to determine if consumers’ responses were rationally or emotionally motivated. Across two studies, consumers were less willing to fly when the pilot was taking medication, and when the medication was a high dose opposed to a low dose. Additionally, affect was found to completely mediate the relationship between three of the four medications when compared to the control condition, suggesting that participants’ responses were emotionally driven. Finally, a discussion of the findings and practical implications of the study are provided.</p>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".